Provable Defense

Provable defense in machine learning focuses on developing methods that offer mathematically guaranteed robustness against various adversarial attacks, such as data poisoning, backdoor attacks, and adversarial examples. Current research emphasizes creating certified defenses for diverse model types, including multi-modal models and reinforcement learning agents, often employing techniques like ensemble methods, stateful defenses (monitoring query history), and adversarial training with theoretical guarantees on robustness. This field is crucial for ensuring the reliability and security of machine learning systems deployed in safety-critical applications, ranging from autonomous driving to malware detection, by providing verifiable protection against malicious manipulations.